




'''simple docstring'''


import copy
import os
from typing import TYPE_CHECKING, List, Union


if TYPE_CHECKING:
  pass

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "kakaobrain/align-base": "https://huggingface.co/kakaobrain/align-base/resolve/main/config.json",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """align_text_model"""







   def __init__(						self   ,				UpperCamelCase=3_0522   ,				UpperCamelCase=768   ,				UpperCamelCase=12   ,				UpperCamelCase=12   ,				UpperCamelCase=3072   ,				UpperCamelCase="gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=512   ,				UpperCamelCase=2   ,				UpperCamelCase=0.02   ,				UpperCamelCase=1e-12   ,				UpperCamelCase=0   ,				UpperCamelCase="absolute"   ,				UpperCamelCase=True   ,				**UpperCamelCase   ,				)					->     str:
     super().__init__(**UpperCamelCase   )

     __a								=       vocab_size
     __a								=       hidden_size
     __a								=       num_hidden_layers
     __a								=       num_attention_heads
     __a								=       hidden_act
     __a								=       intermediate_size
     __a								=       hidden_dropout_prob
     __a								=       attention_probs_dropout_prob
     __a								=       max_position_embeddings
     __a								=       type_vocab_size
     __a								=       initializer_range
     __a								=       layer_norm_eps
     __a								=       position_embedding_type
     __a								=       use_cache
     __a								=       pad_token_id







   @classmethod
   def 		UpperCamelCase__      (						cls   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     "PretrainedConfig":
     cls._set_token_in_kwargs(UpperCamelCase   )

     __a						,							__a								=       cls.get_config_dict(UpperCamelCase   ,				**UpperCamelCase   )

     # get the text config dict if we are loading from AlignConfig
     if config_dict.get('model_type'   ) == "align":
       __a								=       config_dict['text_config']

     if "model_type" in config_dict and hasattr(cls   ,				'model_type'   ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
           f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."   )

     return cls.from_dict(UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):
   _a    						=   """align_vision_model"""







   def __init__(						self   ,				UpperCamelCase = 3   ,				UpperCamelCase = 600   ,				UpperCamelCase = 2.0   ,				UpperCamelCase = 3.1   ,				UpperCamelCase = 8   ,				UpperCamelCase = [3, 3, 5, 3, 5, 5, 3]   ,				UpperCamelCase = [32, 16, 24, 40, 80, 112, 192]   ,				UpperCamelCase = [16, 24, 40, 80, 112, 192, 320]   ,				UpperCamelCase = []   ,				UpperCamelCase = [1, 2, 2, 2, 1, 2, 1]   ,				UpperCamelCase = [1, 2, 2, 3, 3, 4, 1]   ,				UpperCamelCase = [1, 6, 6, 6, 6, 6, 6]   ,				UpperCamelCase = 0.25   ,				UpperCamelCase = "swish"   ,				UpperCamelCase = 2560   ,				UpperCamelCase = "mean"   ,				UpperCamelCase = 0.02   ,				UpperCamelCase = 0.001   ,				UpperCamelCase = 0.99   ,				UpperCamelCase = 0.2   ,				**UpperCamelCase   ,				)					->     Optional[Any]:
     super().__init__(**UpperCamelCase   )

     __a								=       num_channels
     __a								=       image_size
     __a								=       width_coefficient
     __a								=       depth_coefficient
     __a								=       depth_divisor
     __a								=       kernel_sizes
     __a								=       in_channels
     __a								=       out_channels
     __a								=       depthwise_padding
     __a								=       strides
     __a								=       num_block_repeats
     __a								=       expand_ratios
     __a								=       squeeze_expansion_ratio
     __a								=       hidden_act
     __a								=       hidden_dim
     __a								=       pooling_type
     __a								=       initializer_range
     __a								=       batch_norm_eps
     __a								=       batch_norm_momentum
     __a								=       drop_connect_rate
     __a								=       sum(UpperCamelCase   ) * 4







   @classmethod
   def 		UpperCamelCase__      (						cls   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     "PretrainedConfig":
     cls._set_token_in_kwargs(UpperCamelCase   )

     __a						,							__a								=       cls.get_config_dict(UpperCamelCase   ,				**UpperCamelCase   )

     # get the vision config dict if we are loading from AlignConfig
     if config_dict.get('model_type'   ) == "align":
       __a								=       config_dict['vision_config']

     if "model_type" in config_dict and hasattr(cls   ,				'model_type'   ) and config_dict["model_type"] != cls.model_type:
       logger.warning(
           f"You are using a model of type {config_dict['model_type']} to instantiate a model of type "
           f"{cls.model_type}. This is not supported for all configurations of models and can yield errors."   )

     return cls.from_dict(UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):
   _a    						=   """align"""
   _a    						=   True







   def __init__(						self   ,				UpperCamelCase=None   ,				UpperCamelCase=None   ,				UpperCamelCase=640   ,				UpperCamelCase=1.0   ,				UpperCamelCase=0.02   ,				**UpperCamelCase   ,				)					->     Union[str, Any]:
     super().__init__(**UpperCamelCase   )

     if text_config is None:
       __a								=       {}
       logger.info('text_config is None. Initializing the AlignTextConfig with default values.'   )

     if vision_config is None:
       __a								=       {}
       logger.info('vision_config is None. Initializing the AlignVisionConfig with default values.'   )

     __a								=       AlignTextConfig(**UpperCamelCase   )
     __a								=       AlignVisionConfig(**UpperCamelCase   )

     __a								=       projection_dim
     __a								=       temperature_init_value
     __a								=       initializer_range







   @classmethod
   def 		UpperCamelCase__      (						cls   ,				UpperCamelCase   ,				UpperCamelCase   ,				**UpperCamelCase   )					->     List[Any]:
     return cls(text_config=text_config.to_dict()   ,				vision_config=vision_config.to_dict()   ,				**UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     List[Any]:
     __a								=       copy.deepcopy(self.__dict__   )
     __a								=       self.text_config.to_dict()
     __a								=       self.vision_config.to_dict()
     __a								=       self.__class__.model_type
     return output









'''simple docstring'''


import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch

from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search

from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils import ROUGE_KEYS


logging.basicConfig(level=logging.DEBUG)
UpperCAmelCase_					=   logging.getLogger()


def     SCREAMING_SNAKE_CASE							(   a_						:				Path					,       a_						:				list      ):
  __a								=       '\n'.join(a_      )
  Path(a_      ).open('w'      ).writelines(a_      )


UpperCAmelCase_					=   "patrickvonplaten/t5-tiny-random"
UpperCAmelCase_					=   "sshleifer/bart-tiny-random"
UpperCAmelCase_					=   "sshleifer/tiny-mbart"

UpperCAmelCase_					=   logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)
logging.disable(logging.CRITICAL)  # remove noisy download output from tracebacks





class  __lowercase    (	__magic_name__			):







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()
     __a								=       [' New York (CNN)When Liana Barrientos was 23 years old, she got married in Westchester County.']
     _dump_articles(UpperCamelCase   ,				UpperCamelCase   )

     __a								=       str(Path(self.get_auto_remove_tmp_dir()   ) / 'scores.json'   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {input_file_name}\n            {output_file_name}\n            --score_path {score_path}\n            --task {task}\n            --num_beams 2\n            --length_penalty 2.0\n            ".split()

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       run_generate()
       assert Path(UpperCamelCase   ).exists()
       # os.remove(Path(output_file_name))







   def 		UpperCamelCase__      (						self   )					->     int:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([BART_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Union[str, Any]:
     self.run_eval_tester(UpperCamelCase   )







   @parameterized.expand([T5_TINY, MBART_TINY]   )
   @slow
   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Optional[Any]:
     __a								=       Path(self.get_auto_remove_tmp_dir()   ) / 'utest_input.source'
     __a								=       input_file_name.parent / 'utest_output.txt'
     assert not output_file_name.exists()

     __a								=       {
         'en': ['Machine learning is great, isn\'t it?', 'I like to eat bananas', 'Tomorrow is another great day!'],
         'de': [
             'Maschinelles Lernen ist großartig, oder?',
             'Ich esse gerne Bananen',
             'Morgen ist wieder ein toller Tag!',
         ],
     }

     __a								=       Path(self.get_auto_remove_tmp_dir()   )
     __a								=       str(tmp_dir / 'scores.json'   )
     __a								=       str(tmp_dir / 'val.target'   )
     _dump_articles(UpperCamelCase   ,				text['en']   )
     _dump_articles(UpperCamelCase   ,				text['de']   )
     __a								=       'translation_en_to_de' if model == T5_TINY else 'summarization'
     __a								=       f"\n            run_eval_search.py\n            {model}\n            {str(UpperCamelCase   )}\n            {str(UpperCamelCase   )}\n            --score_path {score_path}\n            --reference_path {reference_path}\n            --task {task}\n            ".split()
     testargs.extend(['--search', 'num_beams=1:2 length_penalty=0.9:1.0']   )

     with patch.object(UpperCamelCase   ,				'argv'   ,				UpperCamelCase   ):
       with CaptureStdout() as cs:
         run_search()
       __a								=       [' num_beams | length_penalty', model, 'Best score args']
       __a								=       ['Info']
       if "translation" in task:
         expected_strings.append('bleu'   )
       else:
         expected_strings.extend(UpperCamelCase   )
       for w in expected_strings:
         assert w in cs.out
       for w in un_expected_strings:
         assert w not in cs.out
       assert Path(UpperCamelCase   ).exists()
       os.remove(Path(UpperCamelCase   )   )



